The association of self-perceptions of aging, cognitive reserve, and depression among older adults: A network analysis approach.

AIM: Evidence shows that self-perceptions of aging and cognitive reserve are respectively risk and protective factors for depression. However, prior research has largely relied on traditional regression analyses without adopting a systems perspective. Network analysis can reveal complex association patterns in depression-related systems. This study aimed to identify core and bridge nodes in a depression-related network model and examine the importance of self-perceptions of aging.

METHODS

A survey was conducted with 1019 participants from March to October 2023. Network analysis was conducted in R 4.4.2 using a Gaussian Graphical Model (GGM) with graphical LASSO (GLASSO) regularization and the Extended Bayesian Information Criterion (EBIC) for model selection.

RESULTS

(1) According to expected influence (EI) values (a node centrality measure), the core nodes (i.e., the most influential nodes within the mixed-level network) were: ph4 (Fatigue) (EI=1.481), ph2 (Depressed or sad mood) (EI=1.156), ph6 (Feeling of worthlessness) (EI=1.107), ph1 (Anhedonia) (EI=1.034), ER (Emotional representations) (EI=0.966). (2) Based on bridge expected influence (BEI) values (a measure of a node's role in connecting different communities), the bridge nodes (i.e., nodes that connect the cognitive reserve and depression communities) were: cr3 (Leisure activities) (BEI=-3.120), ER (Emotional representations) (BEI=1.546), and ph1 (Anhedonia) (BEI=0.945). (3) Network comparison results showed that the overall network strength was significantly higher in the negative self-perceptions of aging group than in the positive group (4.23 vs. 3.10; S = 1.43, p = 0.020).

CONCLUSIONS

The identified core and bridge nodes are potential intervention targets. Self-perceptions of aging played a key role in older adults' mental health.